mirror of
https://github.com/trustgraph-ai/trustgraph.git
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Updated test suite for explainability & provenance (#696)
* Provenance tests * Embeddings tests * Test librarian * Test triples stream * Test concurrency * Entity centric graph writes * Agent tool service tests * Structured data tests * RDF tests * Addition LLM tests * Reliability tests
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182
tests/unit/test_text_completion/test_azure_openai_streaming.py
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182
tests/unit/test_text_completion/test_azure_openai_streaming.py
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"""
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Tests for Azure OpenAI streaming: model/temperature override during streaming,
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RateLimitError → TooManyRequests conversion, chunk iteration, and final token
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count emission.
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"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from unittest import IsolatedAsyncioTestCase
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from trustgraph.model.text_completion.azure_openai.llm import Processor
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from trustgraph.base import LlmChunk
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from trustgraph.exceptions import TooManyRequests
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def _make_processor(mock_azure_openai_class, model="gpt-4"):
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"""Create a Processor with mocked base classes."""
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with patch('trustgraph.base.async_processor.AsyncProcessor.__init__',
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return_value=None), \
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patch('trustgraph.base.llm_service.LlmService.__init__',
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return_value=None):
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proc = Processor(
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endpoint="https://test.openai.azure.com/",
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token="test-token",
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api_version="2024-12-01-preview",
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model=model,
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temperature=0.0,
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max_output=4192,
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concurrency=1,
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taskgroup=AsyncMock(),
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id="test-processor",
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)
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return proc
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def _make_stream_chunk(content=None, usage=None):
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"""Create a mock streaming chunk."""
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chunk = MagicMock()
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if content:
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chunk.choices = [MagicMock()]
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chunk.choices[0].delta.content = content
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else:
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chunk.choices = []
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chunk.usage = usage
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return chunk
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class TestAzureOpenAIStreaming(IsolatedAsyncioTestCase):
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_streaming_yields_chunks(self, mock_azure_class):
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class)
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usage = MagicMock()
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usage.prompt_tokens = 10
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usage.completion_tokens = 5
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stream_data = [
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_make_stream_chunk(content="Hello"),
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_make_stream_chunk(content=" world"),
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_make_stream_chunk(usage=usage),
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]
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mock_client.chat.completions.create.return_value = iter(stream_data)
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results = []
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async for chunk in proc.generate_content_stream("sys", "user"):
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results.append(chunk)
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assert len(results) == 3 # 2 content + 1 final
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assert results[0].text == "Hello"
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assert results[0].is_final is False
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assert results[1].text == " world"
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assert results[2].is_final is True
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assert results[2].in_token == 10
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assert results[2].out_token == 5
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_streaming_model_override(self, mock_azure_class):
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class, model="gpt-4")
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usage = MagicMock()
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usage.prompt_tokens = 5
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usage.completion_tokens = 2
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stream_data = [
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_make_stream_chunk(content="ok"),
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_make_stream_chunk(usage=usage),
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]
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mock_client.chat.completions.create.return_value = iter(stream_data)
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results = []
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async for chunk in proc.generate_content_stream(
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"sys", "user", model="gpt-4o"
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):
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results.append(chunk)
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# All chunks carry overridden model
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for r in results:
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assert r.model == "gpt-4o"
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# Verify API call used overridden model
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call_kwargs = mock_client.chat.completions.create.call_args[1]
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assert call_kwargs["model"] == "gpt-4o"
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_streaming_temperature_override(self, mock_azure_class):
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class)
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usage = MagicMock()
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usage.prompt_tokens = 5
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usage.completion_tokens = 2
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stream_data = [_make_stream_chunk(usage=usage)]
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mock_client.chat.completions.create.return_value = iter(stream_data)
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async for _ in proc.generate_content_stream(
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"sys", "user", temperature=0.7
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):
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pass
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call_kwargs = mock_client.chat.completions.create.call_args[1]
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assert call_kwargs["temperature"] == 0.7
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_streaming_rate_limit_raises_too_many_requests(self, mock_azure_class):
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from openai import RateLimitError
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class)
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mock_client.chat.completions.create.side_effect = RateLimitError(
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"Rate limit exceeded", response=MagicMock(), body=None
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)
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with pytest.raises(TooManyRequests):
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async for _ in proc.generate_content_stream("sys", "user"):
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pass
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_streaming_generic_exception_propagates(self, mock_azure_class):
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class)
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mock_client.chat.completions.create.side_effect = Exception("API down")
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with pytest.raises(Exception, match="API down"):
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async for _ in proc.generate_content_stream("sys", "user"):
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pass
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_streaming_passes_stream_options(self, mock_azure_class):
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class)
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usage = MagicMock()
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usage.prompt_tokens = 0
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usage.completion_tokens = 0
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stream_data = [_make_stream_chunk(usage=usage)]
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mock_client.chat.completions.create.return_value = iter(stream_data)
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async for _ in proc.generate_content_stream("sys", "user"):
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pass
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call_kwargs = mock_client.chat.completions.create.call_args[1]
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assert call_kwargs["stream"] is True
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assert call_kwargs["stream_options"] == {"include_usage": True}
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@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
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async def test_supports_streaming(self, mock_azure_class):
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mock_client = MagicMock()
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mock_azure_class.return_value = mock_client
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proc = _make_processor(mock_azure_class)
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assert proc.supports_streaming() is True
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199
tests/unit/test_text_completion/test_azure_streaming.py
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199
tests/unit/test_text_completion/test_azure_streaming.py
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"""
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Tests for Azure serverless endpoint streaming: model override during streaming,
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HTTP 429 during streaming, SSE chunk parsing, and final token count emission.
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"""
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import json
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from unittest import IsolatedAsyncioTestCase
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from trustgraph.model.text_completion.azure.llm import Processor
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from trustgraph.base import LlmChunk
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from trustgraph.exceptions import TooManyRequests
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def _make_processor(mock_requests, model="AzureAI", temperature=0.0):
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"""Create a Processor with mocked base classes."""
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with patch('trustgraph.base.async_processor.AsyncProcessor.__init__',
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return_value=None), \
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patch('trustgraph.base.llm_service.LlmService.__init__',
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return_value=None):
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proc = Processor(
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endpoint="https://test.azure.com/v1/chat/completions",
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token="test-token",
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temperature=temperature,
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max_output=4192,
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model=model,
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concurrency=1,
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taskgroup=AsyncMock(),
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id="test-processor",
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)
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return proc
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def _sse_lines(*data_items):
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"""Build SSE byte lines from data items. '[DONE]' is appended."""
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lines = []
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for item in data_items:
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if isinstance(item, dict):
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lines.append(f"data: {json.dumps(item)}".encode())
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else:
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lines.append(f"data: {item}".encode())
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lines.append(b"data: [DONE]")
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return lines
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class TestAzureServerlessStreaming(IsolatedAsyncioTestCase):
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_yields_chunks(self, mock_requests):
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proc = _make_processor(mock_requests)
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chunks = [
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{"choices": [{"delta": {"content": "Hello"}}]},
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{"choices": [{"delta": {"content": " world"}}]},
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{"usage": {"prompt_tokens": 10, "completion_tokens": 5}},
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]
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.iter_lines.return_value = _sse_lines(*chunks)
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mock_requests.post.return_value = mock_response
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results = []
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async for chunk in proc.generate_content_stream("sys", "user"):
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results.append(chunk)
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# Content chunks + final chunk
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assert len(results) == 3
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assert results[0].text == "Hello"
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assert results[0].is_final is False
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assert results[1].text == " world"
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assert results[1].is_final is False
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assert results[2].is_final is True
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assert results[2].in_token == 10
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assert results[2].out_token == 5
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_model_override(self, mock_requests):
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proc = _make_processor(mock_requests, model="default-model")
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.iter_lines.return_value = _sse_lines(
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{"choices": [{"delta": {"content": "ok"}}]},
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{"usage": {"prompt_tokens": 5, "completion_tokens": 2}},
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)
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mock_requests.post.return_value = mock_response
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results = []
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async for chunk in proc.generate_content_stream(
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"sys", "user", model="override-model"
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):
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results.append(chunk)
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# All chunks should carry the overridden model name
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for r in results:
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assert r.model == "override-model"
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# Verify the request body used the overridden model
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call_args = mock_requests.post.call_args
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body = json.loads(call_args[1]["data"])
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assert body["model"] == "override-model"
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_temperature_override(self, mock_requests):
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proc = _make_processor(mock_requests, temperature=0.0)
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.iter_lines.return_value = _sse_lines(
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{"choices": [{"delta": {"content": "ok"}}]},
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{"usage": {"prompt_tokens": 5, "completion_tokens": 2}},
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)
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mock_requests.post.return_value = mock_response
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results = []
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async for chunk in proc.generate_content_stream(
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"sys", "user", temperature=0.9
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):
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results.append(chunk)
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call_args = mock_requests.post.call_args
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body = json.loads(call_args[1]["data"])
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assert body["temperature"] == 0.9
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_429_raises_too_many_requests(self, mock_requests):
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proc = _make_processor(mock_requests)
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mock_response = MagicMock()
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mock_response.status_code = 429
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mock_requests.post.return_value = mock_response
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with pytest.raises(TooManyRequests):
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async for _ in proc.generate_content_stream("sys", "user"):
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pass
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_http_error_raises_runtime(self, mock_requests):
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proc = _make_processor(mock_requests)
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mock_response = MagicMock()
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mock_response.status_code = 503
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mock_response.text = "Service Unavailable"
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mock_requests.post.return_value = mock_response
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with pytest.raises(RuntimeError, match="HTTP 503"):
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async for _ in proc.generate_content_stream("sys", "user"):
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pass
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_includes_stream_options(self, mock_requests):
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"""Verify stream=True and stream_options in request body."""
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proc = _make_processor(mock_requests)
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.iter_lines.return_value = _sse_lines(
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{"usage": {"prompt_tokens": 0, "completion_tokens": 0}},
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)
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mock_requests.post.return_value = mock_response
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async for _ in proc.generate_content_stream("sys", "user"):
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pass
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call_args = mock_requests.post.call_args
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body = json.loads(call_args[1]["data"])
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assert body["stream"] is True
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assert body["stream_options"]["include_usage"] is True
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_malformed_json_skipped(self, mock_requests):
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"""Malformed JSON chunks should be skipped, not crash the stream."""
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proc = _make_processor(mock_requests)
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mock_response = MagicMock()
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mock_response.status_code = 200
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lines = [
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b"data: {not valid json}",
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f'data: {json.dumps({"choices": [{"delta": {"content": "ok"}}]})}'.encode(),
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f'data: {json.dumps({"usage": {"prompt_tokens": 1, "completion_tokens": 1}})}'.encode(),
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b"data: [DONE]",
|
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]
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mock_response.iter_lines.return_value = lines
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mock_requests.post.return_value = mock_response
|
||||
|
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results = []
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async for chunk in proc.generate_content_stream("sys", "user"):
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results.append(chunk)
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|
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# Should get the valid content chunk + final chunk
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assert any(r.text == "ok" for r in results)
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assert results[-1].is_final is True
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@patch('trustgraph.model.text_completion.azure.llm.requests')
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async def test_streaming_supports_streaming_flag(self, mock_requests):
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proc = _make_processor(mock_requests)
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assert proc.supports_streaming() is True
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140
tests/unit/test_text_completion/test_rate_limit_contract.py
Normal file
140
tests/unit/test_text_completion/test_rate_limit_contract.py
Normal file
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|
@ -0,0 +1,140 @@
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"""
|
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Cross-provider rate limit contract tests: verify that every LLM provider
|
||||
that handles rate limits converts its provider-specific exception to
|
||||
TooManyRequests consistently.
|
||||
|
||||
Also tests the client-side error translation in the base client.
|
||||
"""
|
||||
|
||||
import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from unittest import IsolatedAsyncioTestCase
|
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|
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from trustgraph.exceptions import TooManyRequests
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||||
|
||||
|
||||
class TestAzureServerless429(IsolatedAsyncioTestCase):
|
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"""Azure serverless endpoint: HTTP 429 → TooManyRequests"""
|
||||
|
||||
@patch('trustgraph.model.text_completion.azure.llm.requests')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__', return_value=None)
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__', return_value=None)
|
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async def test_http_429_raises_too_many_requests(self, _llm, _async, mock_requests):
|
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from trustgraph.model.text_completion.azure.llm import Processor
|
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proc = Processor(
|
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endpoint="https://test.azure.com/v1/chat",
|
||||
token="t", concurrency=1, taskgroup=AsyncMock(), id="t",
|
||||
)
|
||||
mock_response = MagicMock()
|
||||
mock_response.status_code = 429
|
||||
mock_requests.post.return_value = mock_response
|
||||
|
||||
with pytest.raises(TooManyRequests):
|
||||
await proc.generate_content("sys", "prompt")
|
||||
|
||||
|
||||
class TestAzureOpenAIRateLimit(IsolatedAsyncioTestCase):
|
||||
"""Azure OpenAI: openai.RateLimitError → TooManyRequests"""
|
||||
|
||||
@patch('trustgraph.model.text_completion.azure_openai.llm.AzureOpenAI')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__', return_value=None)
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__', return_value=None)
|
||||
async def test_rate_limit_error_raises_too_many_requests(self, _llm, _async, mock_cls):
|
||||
from openai import RateLimitError
|
||||
from trustgraph.model.text_completion.azure_openai.llm import Processor
|
||||
mock_client = MagicMock()
|
||||
mock_cls.return_value = mock_client
|
||||
proc = Processor(
|
||||
endpoint="https://test.openai.azure.com/", token="t",
|
||||
model="gpt-4", concurrency=1, taskgroup=AsyncMock(), id="t",
|
||||
)
|
||||
mock_client.chat.completions.create.side_effect = RateLimitError(
|
||||
"rate limited", response=MagicMock(), body=None
|
||||
)
|
||||
|
||||
with pytest.raises(TooManyRequests):
|
||||
await proc.generate_content("sys", "prompt")
|
||||
|
||||
|
||||
class TestOpenAIRateLimit(IsolatedAsyncioTestCase):
|
||||
"""OpenAI: openai.RateLimitError → TooManyRequests"""
|
||||
|
||||
@patch('trustgraph.model.text_completion.openai.llm.OpenAI')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__', return_value=None)
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__', return_value=None)
|
||||
async def test_rate_limit_error_raises_too_many_requests(self, _llm, _async, mock_cls):
|
||||
from openai import RateLimitError
|
||||
from trustgraph.model.text_completion.openai.llm import Processor
|
||||
mock_client = MagicMock()
|
||||
mock_cls.return_value = mock_client
|
||||
proc = Processor(
|
||||
api_key="k", concurrency=1, taskgroup=AsyncMock(), id="t",
|
||||
)
|
||||
mock_client.chat.completions.create.side_effect = RateLimitError(
|
||||
"rate limited", response=MagicMock(), body=None
|
||||
)
|
||||
|
||||
with pytest.raises(TooManyRequests):
|
||||
await proc.generate_content("sys", "prompt")
|
||||
|
||||
|
||||
class TestClaudeRateLimit(IsolatedAsyncioTestCase):
|
||||
"""Claude/Anthropic: anthropic.RateLimitError → TooManyRequests"""
|
||||
|
||||
@patch('trustgraph.model.text_completion.claude.llm.anthropic')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__', return_value=None)
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__', return_value=None)
|
||||
async def test_rate_limit_error_raises_too_many_requests(self, _llm, _async, mock_anthropic):
|
||||
from trustgraph.model.text_completion.claude.llm import Processor
|
||||
|
||||
mock_client = MagicMock()
|
||||
mock_anthropic.Anthropic.return_value = mock_client
|
||||
|
||||
proc = Processor(
|
||||
api_key="k", concurrency=1, taskgroup=AsyncMock(), id="t",
|
||||
)
|
||||
|
||||
mock_anthropic.RateLimitError = type("RateLimitError", (Exception,), {})
|
||||
mock_client.messages.create.side_effect = mock_anthropic.RateLimitError(
|
||||
"rate limited"
|
||||
)
|
||||
|
||||
with pytest.raises(TooManyRequests):
|
||||
await proc.generate_content("sys", "prompt")
|
||||
|
||||
|
||||
class TestCohereRateLimit(IsolatedAsyncioTestCase):
|
||||
"""Cohere: cohere.TooManyRequestsError → TooManyRequests"""
|
||||
|
||||
@patch('trustgraph.model.text_completion.cohere.llm.cohere')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__', return_value=None)
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__', return_value=None)
|
||||
async def test_rate_limit_error_raises_too_many_requests(self, _llm, _async, mock_cohere):
|
||||
from trustgraph.model.text_completion.cohere.llm import Processor
|
||||
|
||||
mock_client = MagicMock()
|
||||
mock_cohere.Client.return_value = mock_client
|
||||
|
||||
proc = Processor(
|
||||
api_key="k", concurrency=1, taskgroup=AsyncMock(), id="t",
|
||||
)
|
||||
|
||||
mock_cohere.TooManyRequestsError = type(
|
||||
"TooManyRequestsError", (Exception,), {}
|
||||
)
|
||||
mock_client.chat.side_effect = mock_cohere.TooManyRequestsError(
|
||||
"rate limited"
|
||||
)
|
||||
|
||||
with pytest.raises(TooManyRequests):
|
||||
await proc.generate_content("sys", "prompt")
|
||||
|
||||
|
||||
class TestClientSideRateLimitTranslation:
|
||||
"""Client base class: error type 'too-many-requests' → TooManyRequests"""
|
||||
|
||||
def test_error_type_mapping(self):
|
||||
"""The wire format error type string is 'too-many-requests'."""
|
||||
from trustgraph.schema import Error
|
||||
err = Error(type="too-many-requests", message="slow down")
|
||||
assert err.type == "too-many-requests"
|
||||
Loading…
Add table
Add a link
Reference in a new issue